There has been a dramatic increase in demand for mobile connectivity solutions utilizing various wireless components and WLANs. Such networks generally involve the use of wireless access points (APs) configured to communicate with mobile devices using one or more RF channels in accordance with various wireless standards and protocols.
It is often desirable to determine the physical location of a particular mobile device within a network. One way of accomplishing this task is to examine signal strength information related to the access points and/or the mobile units within the network environment, thus allowing the location to be inferred with an acceptable degree of accuracy.
In this regard, there are a number of known algorithms that use signal strength information (e.g., RSSI values) from a wireless device to help determine the location of another device. For example, in the 802.11 wireless environment, the signal strengths from the APs are typically used in combination with triangulation techniques to estimate the location of the mobile devices in the area.
Two popular methods of location prediction are the mathematical modeling approach and the “fingerprinting” approach. In the mathematical modeling approach, the AP transmit power and antenna gain are used to determine signal coverage, or a “heat map.” In general, RSSI values between an AP and a mobile device are proportional to the distance of the mobile device from the AP. A mathematical model may thus be used to generate AP signal strength contours that correspond to the mobile device RSSI. The relationship between the mobile device signal strength and the AP signal strength are known; however, the coverage areas associated with the AP and the mobile device are not congruent (i.e., not the same shape and/or size). This results in significant prediction error in the mathematical model.
In the fingerprinting approach, a lookup table (fingerprint data) is populated using test data generated using a test mobile device. During real-time locationing, however, the actual mobile device under consideration might have much different RF properties than the mobile device that was used for fingerprint data collection.
Accordingly, there is a need for improved methods and systems for better determining the location of wireless devices in a network.